Exposing.ai
Annotated Faces in The Wild
Three of 205 people from the Annotated Faces in The Wild (AFW) face recognition. Faces are blurred to protect privacy. Visualization by Adam Harvey / Exposing.ai licensed under CC-BY-NC with original images licensed and attributed under Creative Commons CC-BY (attribution required, no commercial use).
Three of 205 people from the Annotated Faces in The Wild (AFW) face recognition. Faces are blurred to protect privacy. Visualization by Adam Harvey / Exposing.ai licensed under CC-BY-NC with original images licensed and attributed under Creative Commons CC-BY (attribution required, no commercial use).

Annotated Faces in the Wild

Annotated Faces in the Wild (AFW) is a dataset of photos used for face recognition. The dataset was published in 2012 and contains 205 total images. Exposing.ai located 182 original photos from Flickr used to build AFW. The dataset has been used in at least 118 projects spanning 21 countries, including 15 projects that may have commercial applications, and 1 project that may have defense applications.

In 2013 AFW appeared in a research paper from Megvii (Face++), who is currently blacklisted in the United States. 2 AFW also appears in several experiments with researchers affiliated with Microsoft, even though most of the images were either copyright or used a Creative Commons Non-Commercial license.

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Information Supply Chain

To help understand how Annotated Faces in The Wild has been used around the world by commercial, military, and academic organizations; existing publicly available research citing Annotated Faces in the Wild was collected, verified, and geocoded to show how AI training data has proliferated around the world. Click on the markers to reveal research projects at that location.

Citation data is collected using SemanticScholar.org then dataset usage verified and geolocated. Citations are used to provide an estimated overview of how and where images were used based on institutional affiliations. Thicker lines represent more citations. Please zoom in to see all institutions, as cities may have multiple points very close together.

AFW Copyright Distribution

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AFW Creative Commons license distribution | Download Data (CSV) | Download Chart (SVG)

AFW Creative Commons License Distribution

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AFW Creative Commons license distribution | Download Data (CSV) | Download Chart (SVG)

AFW Image Upload Year Distribution

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AFW Creative Commons license distribution | Download Data (CSV) | Download Chart (SVG)

Top 10 AFW Image #Tags

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Top 10 image #tags used in AFW | Download Data (CSV) | Download Chart (SVG)

Top 10 Geocoded Cities AFW

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Top 10 cities for geocoded photos in AFW | Download Data (CSV) | Download Chart (SVG)

Citing This Work

If you reference or use any data from the Exposing.ai project, cite our original research as follows:

@online{Exposing.ai,
  author = {Harvey, Adam. LaPlace, Jules.},
  title = {Exposing.ai},
  year = 2021,
  url = {https://exposing.ai},
  urldate = {2021-01-01}
}

If you reference or use any data from AFW cite the author's work:

@article{Zhu2012FaceDP,
    author = "Zhu, Xiangxin and Ramanan, Deva",
    title = "Face detection, pose estimation, and landmark localization in the wild",
    journal = "2012 IEEE Conference on Computer Vision and Pattern Recognition",
    year = "2012",
    pages = "2879-2886"
}

References

  • 1 Xiangxin Zhu, et al. "Face detection, pose estimation, and landmark localization in the wild". 2012 IEEE Conference on Computer Vision and Pattern Recognition. (2012): 2879-2886.
  • 2 aErjin Zhou, et al. "Extensive Facial Landmark Localization with Coarse-to-Fine Convolutional Network Cascade". 2013 IEEE International Conference on Computer Vision Workshops. (2013): 386-391.